class_docstr: Randomly zeroes some of the elements of the input
  tensor with probability :attr:`p` using samples from a Bernoulli
  distribution. Each channel will be zeroed out independently on every forward
  call.
  This has proven to be an effective technique for regularization and
  preventing the co-adaptation of neurons as described in the paper
  `Improving neural networks by preventing co-adaptation of feature
  detectors`_ .
  Furthermore, the outputs are scaled by a factor of :math:`\frac{1}{1-p}` during
  training.
  Shape:
      - Input: :math:`(*)`. Input can be of any shape.
      - Output: :math:`(*)`. Output is of the same shape as input.
  
  Args:
      p (float, optional):
          Probability of an element to be zeroed. Default: 0.5
      inplace (bool, optional):
          If set to ``True``, will do this operation in-place. Default: ``False``
  
  Examples::
      >>> m = nn.Dropout(p=0.2)
      >>> input = torch.randn(20, 16)
      >>> output = m(input)
  .. _Improving neural networks by preventing co-adaptation of feature
      detectors: https://arxiv.org/abs/1207.0580
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code: def forward(self, input):
      return F.dropout(input, self.p, True, self.inplace)
  
file_path: baal/baal/bayesian/dropout.py
imports: from typing import Optional
  import torch
  from torch import nn
  from torch.nn import functional
  from torch.nn.modules.dropout import _DropoutNd
  from baal.bayesian.common import BayesianModule, _patching_wrapper
label_desc: Use image node_id 1 for calling the Dropout obj's underlying member method code with example usage: obj.forward(input) and returns: F